Moreover, we investigate recent methods on this dataset, including fully supervised segmentation methods and annotation-efficient methods. Specifically, we first evaluate several state-of-the-art medical segmentation methods on this data, like nnUNet [1]}, Attention UNet [2]}, DeepLabV3+ [3]}, UNet++ [4]} and ResUNet [5]}. After that, we investigate the pre-trained model generalization ability on two open datasets. Furthermore, we do the user study on this dataset to measure the gap between deep learning models and real oncologists. Finally, we introduce a weakly supervised abdominal organs segmentation method with scribble annotations, which is desirable to reduce the annotation costs in the future. These attempts can be used as a new abdominal organs segmentation benchmark for further research.
